PCIPG2.0: multi-omics fusion and structure-aware graph autoencoding for protein complex identification
Yixiang Huang1, Jiudong Wang2, Lei Yang1
1School of Mathematics, Renmin University of China, Beijing 100872, China.
Bioinformatics (Oxford, England)
|July 25, 2026
Summary
This study introduces PCIPG 2.0, a novel framework for identifying protein complexes by integrating multi-omics data to enhance protein-protein interaction networks and employing residue-informed learning for improved accuracy and mechanistic interpretability.
Area of Science:
- Computational Biology
- Systems Biology
- Bioinformatics
Background:
- Identifying protein complexes is crucial for understanding cellular functions but is hindered by incomplete protein-protein interaction (PPI) networks and lack of mechanistic interpretability in current clustering methods.
- Existing approaches struggle with missing interactions and limited specificity, impacting the accuracy of protein complex discovery.
Purpose of the Study:
- To present PCIPG 2.0, an unsupervised framework designed to overcome key challenges in protein complex identification from PPI networks.
- To improve the accuracy and mechanistic understanding of protein complex discovery by addressing incomplete interactomes and enhancing specificity.
Main Methods:
- PCIPG 2.0 integrates multiple omics data to prioritize high-confidence protein associations and augment the interactome.
- The framework learns structure-aware node representations by aggregating residue embeddings on residue graphs.
- A PPI-level graph autoencoder is utilized to infer latent complex memberships.
Main Results:
- PCIPG 2.0 demonstrated superior complex recovery across five yeast benchmarks compared to existing methods.
- Predicted complexes exhibited significantly higher Gene Ontology semantic coherence than random sets.
- Case studies and AlphaFold3 analyses confirmed the functional coherence and structural plausibility of predicted protein assemblies.
Conclusions:
- The combination of multi-omics-driven interactome completion and residue-informed representation learning offers a powerful framework for protein complex identification.
- PCIPG 2.0 provides a mechanistically informed approach to protein complex discovery, even with incomplete interactome data.
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